![]() ![]() Spotify doesn’t have a fixed dictionary for this, but the system is able to identify new music terms as and when they come up – not just in English, but also in Latin-derived languages across cultures. Each term is assigned a weight, reflecting its relative importance in terms of how many times an individual would attribute that term to a song or musician they like. ![]() Every artist and song is associated with thousands of top terms that are subject to change on a daily basis. These keywords are then categorised into “cultural vectors” and “top terms”. It looks at what people are saying about certain artists or songs and the language being used, and also which other artists and songs are being discussed alongside, if at all, and identifies descriptive terms, noun phrases and other texts associated with those songs or artists. Spotify’s AI scans a track’s metadata, as well as blog posts and discussions about specific musicians, and news articles about songs or artists on the internet. While Spotify doesn’t incorporate a rating system for songs, they do use implicit feedback – like the number of times a user has played a particular song, saved a song to their lists, or clicked on the artist’s page upon listening to the song – to provide relevant recommendations for other users that have been deemed similar. ![]() Content streaming platform Netflix similarly adopts collaborative filtering to power their recommendation models, using viewers’ star-based movie ratings to create recommendations for other similar users. This involves comparing a user’s behavioral trends with those of other users. In order for Spotify to generate the ‘Discover Weekly’ personalized music list, the team uses a combination of three models: Collaborative Filtering: Not only does it keep users returning, it also enables greater exposure for artists who users may not search for organically. Machine learning enables the recommendations to improve over time. The recommended playlist comprises tracks that user might have not heard before, but the recommendations are generated based on the user’s search history pattern and potential music preference. Each Monday individual users are presented with a customised list of thirty songs. One example is ‘Discover Weekly’, which reached 40 million people in the first year it was introduced. This information is used to train algorithms which extrapolate relevant insights both from content on the platform and from online conversations about music and artists, as well as from customer data, and use this to enhance the user experience. With tens of millions of users listening to music every minute of the day, brands like Spotify accumulate a mountain of implicit customer data comprised of song preferences, keyword preferences, playlist data, geographic location of listeners, most used devices and more.ĭata drives decisions across every department at Spotify. Why data is the magic ingredient for music streaming success ![]()
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